TODAY’S STUDY: TRENDS IN WIND'S HARDWARE

Understanding Trends in Wind Turbine Prices Over the Past Decade
Mark Bolinger and Ryan Wiser, October 2011 (Lawrence Berkeley National Laboratory)

Executive Summary

Berkeley Lab has gathered price data on 81 U.S. wind turbine transactions totaling 23,850 MW announced from 1997 through early 2011. Figure ES-1 depicts these reported wind turbine transaction prices (along with the associated trend line), broken out by the size of the transaction (in MW). Figure ES-1 also presents average (global) turbine prices reported by Vestas for the years 2005 through 2010, as well as a range of reported pricing (among various turbine manufacturers) for transactions signed in 2010 and so far in 2011 (with 2011 prices generally lower than 2010 prices).

After hitting a low of roughly $750/kW from 2000 to 2002, average wind turbine prices doubled through 2008, rising to an average of roughly $1,500/kW. Wind turbine prices have since declined substantially, with price quotes for transactions executed in 2010 and to date in 2011 ranging from $900-$1,400/kW depending on the manufacturer and turbine model. For example, turbines designed for lower wind speed sites – deploying higher hub heights and larger rotor diameters for a given nameplate capacity – are priced at the higher end of this range. These quotes suggest price declines of as much as 33% or more since late 2008, with an average decline closer to perhaps 20% for orders announced in 2010 (as opposed to in 2011, which has seen further price declines).

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These two substantial and opposing wind turbine price trends over the past decade – and particularly the doubling in prices in the 2002-2008 period – run counter to the smooth, gradually declining technology cost trajectories that are often assumed by energy analysts modeling the diffusion of new technologies, including wind power. Understanding and explaining this notable discrepancy between theory and historical reality is the primary motivation for this work.

Taking a bottom-up approach, this report examines seven primary drivers of wind turbine prices in the United States, with the goal of estimating the degree to which each contributed to the doubling in turbine prices from 2002 through 2008, as well as the subsequent decline in prices through 2010 (our analysis does not extend into 2011 because several of these drivers are best gauged on a full-year basis due to seasonality issues). The first four of these drivers can be considered, at least to some degree, endogenous influences – i.e., those that are largely within the control of the wind industry – and include changes in:

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1) Labor costs, which have historically risen during times of tight turbine supply;

2) Warranty provisions, which reflect technology performance and reliability, and are most often capitalized in turbine prices;

3) Turbine manufacturer profitability, which can impact turbine prices independently of costs; and

4) Turbine design, which for the purpose of this analysis is principally manifested through increased turbine size.

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The other three drivers analyzed in this study can be considered exogenous influences, in that they can impact wind turbine costs but fall mostly outside of the direct control of the wind industry. These exogenous drivers include changes in:

5) Raw materials prices, which affect the cost of inputs to the manufacturing process;

6) Energy prices, which impact the cost of manufacturing and transporting turbines; and

7) Foreign exchange rates, which can impact the dollar amount paid for turbines and components imported into the United States.

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The individual impacts of each of these seven drivers of wind turbine prices are summarized in Table ES-1. To focus attention on the overall trends, the table presents cumulative impacts over the two periods of major turbine price movements in the past decade – i.e., the doubling in turbine prices from 2002-2008, and the subsequent decline in prices through 2010.

Figure ES-2, meanwhile, shows yearly impacts, but of all seven drivers combined rather than individually. The aggregate impact (blue line with circle markers) is plotted against the empirical turbine price trend line from Figure ES-1 (red line with star markers).

In aggregate, these seven drivers explain nearly $600/kW of the ~$750/kW increase in average turbine prices observed from 2002-2008, and nearly $90/kW of the ~$195/kW decrease in 2009 and 2010 (Table ES-1). From 2003 through 2010, the bottom-up analysis of these seven drivers explains between 68% and 89% (depending on the year) of the cumulative empirical price movements (Figure ES-2).

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Nonetheless, Figure ES-2 shows a growing gap between modeled and empirical turbine prices starting in 2005 and increasing through 2007, at which point the gap remains more or less constant until eventually narrowing in 2010. Some portion of this wedge could potentially be explained by:

• Likely increasing labor costs and profitability among component suppliers beginning around 2005; as noted earlier and discussed further in Section 3.3, we quantified these two drivers for turbine manufacturers, but not for their component suppliers;

• Higher exchange rate pass-through than the 50% assumed here (see Appendix C for more discussion of exchange rate pass-through);

• Turbine design and engineering improvements beyond the scaling effects analyzed in Section 3.4; and

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• Methodological issues, such as the necessary reliance on certain data from Vestas when the market was supplied by multiple manufacturers (and especially GE Wind) over this period.

Finally, it is clear from this analysis that there is no single, dominant factor that drove turbine prices higher from 2002-08, or that has yielded lower prices since that time. Turbine up-scaling is, by a significant margin, the largest single driver, although the estimated cost impact associated with up-scaling can be seen as a reasonable expense given the performance improvements garnered by larger turbines. Currency movements are also found to have played a sizable – though somewhat uncertain – role, as have changes in labor costs and material prices. Changes in manufacturer profit margins, warranty provisions, and energy prices are found to have played a less-significant, but non-negligible role.

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Introduction

A considerable literature has developed using learning curve theory to explore how increases in cumulative wind power capacity (and other factors) have historically driven down wind energy costs (for a brief survey of the peer-reviewed literature, see Wiser et al. 2011a). The principal parameter calculated by these studies is the learning rate: for every doubling in cumulative production or installation, the learning rate specifies the associated percentage reduction in costs. Learning rates based on historical data are then often used to forecast future cost developments. As an example, Wiser and Bolinger (2011) calculate a learning rate of 14.4% for the installed cost of wind power projects in the United States during the period between 1982 and 2004, meaning that for each doubling in cumulative installed wind capacity worldwide over this period, installed wind project costs in the U.S. fell by 14.4% on average.

These historical cost reductions, in concert with governmental policies and other drivers, helped to fuel rapid growth in the industry, both domestically and abroad, starting around the turn of the century (Figure 1). In fact, although wind power technology has been commercially available for decades, more than 90% of all wind power capacity both in the US and worldwide has been installed in just the last 10 years. Over this period, global installed wind power capacity more than doubled in the four years from 2002 through 2005, and then again in the three years from 2006 through 2008; it is currently on track to double yet again by late 2011.

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Consistent with standard learning curve theory, the most-recent doubling expected by late 2011 has, in fact, been accompanied by significant cost reductions: as demonstrated later in Section 2, wind turbine prices in the U.S. have fallen somewhere on the order of 20%-33% on average since 2008. By some accounts, these turbine price declines, in combination with improvements in turbine design and performance, will result in a lower cost of wind electricity among projects currently being built than has ever before been possible (Wiser et al. 2011b).

It is important to recognize, however, that the substantial turbine price declines since 2008 started from elevated levels that, themselves, were not consistent with a simple understanding of standard learning curve theory. Rather than the nearly 30% decline in wind project costs that learning curve theory would have expected from 2002 through 2008 as a result of the two doublings in global installed capacity over this period, reported wind project costs in the U.S. actually increased by more than 50% percent over this period, due primarily to a doubling in wind turbine prices. This doubling in wind turbine prices through 2008, and the subsequent decline since then, mark a substantial divergence from the simple application of learning curves to cumulative wind power installations.

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This divergence has important implications for the wind industry, policymakers, research and development (R&D) program managers, and energy analysts. With the wind industry only recently becoming a serious contributor to the power sector in the U.S. and globally, it must take care that unexpected cost inflation does not price wind out of the market, leading to demand destruction. Policymakers who count on wind to provide a growing share of the world’s electricity needs – and who enact policies aimed to achieve that goal – want reassurances that wind can meet this challenge in a cost-effective manner (and that wind will eventually be able to wean itself off of direct public policy support altogether). R&D managers need to understand past cost trends in order to target future research most effectively. Finally, energy analysts who have heretofore placed some faith in the simple application of learning curves to project future technology costs must potentially reevaluate their beliefs and develop a more nuanced understanding of the drivers of wind (and other forms of) power costs.

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Common to all four sets of stakeholders is a growing need to understand what specific factors – if not learning effects – have been driving recent wind power cost trends, and in particular the doubling in wind turbine prices from 2002 through 2008. This article seeks to contribute to such an understanding, with a specific focus on the cost of wind turbines deployed onshore in the United States. In doing so, it builds on the work of other studies that have begun to develop a deeper understanding of historical renewable energy cost drivers beyond simple, traditional concepts of learning (see, e.g., Nemet 2006; Papineau 2006, Ferioli et al. 2009, Yu et al. 2011), as well as those that have examined in some detail other causal influences to wind power costs, both on- and offshore (e.g., Bolinger and Wiser 2009, Milborrow 2008, Berry 2009, Blanco 2009, Greenacre et al. 2010, Carbon Trust 2008, Willow & Valpy 2011, BWEA & Garrad Hassan 2009, Ernst & Young 2009, Dinica 2011).1

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To set the stage, Section 2 documents the increase in onshore wind turbine prices from 2002 through 2008 and the subsequent decline through 2010 using empirical data from the United States, as well as data provided by Vestas – the second-largest wind turbine supplier in the US market over this period. Section 3 examines seven different drivers that have been implicated to varying degrees in the run-up in wind turbine prices through 2008. The first four are, to a degree, endogenous influences that should be influenced by technology learning, and include labor costs, warranty provisions, turbine manufacturer profitability, and turbine design (namely, an increase in turbine nameplate capacity, hub height, and rotor diameter – all of which enable greater energy capture). The other three drivers are, to a greater extent, exogenous influences that fall outside of the scope of traditional learning curve theory, and include changes in raw materials prices, energy prices, and foreign exchange rates. Based on the analysis in Section 3, Section 4 presents the approximate degree to which each of these seven drivers, both individually and in aggregate, is found to have contributed to the overall movement in wind turbine prices over this period. Section 5 concludes by drawing insights from the analysis, and using them to look ahead to 2011 and beyond.

Before proceeding, we emphasize that this article focuses solely on wind turbine prices, rather than on the total installed cost of wind projects (which also includes balance of plant costs) or on the levelized cost of wind generation (which is further affected by financing terms, operating and maintenance expenses, and the amount of electricity generated). Wind turbine costs generally account for roughly 60%-70% of total installed project costs, and a slightly lower percentage of the levelized cost of wind generation (due to the latter also reflecting O&M and financing costs). Though it is ultimately the levelized cost of generation that is the most important of these three cost metrics, understanding trends in wind turbine pricing is a critical element to understanding trends in the levelized cost of generation.

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Aggregate Impact of Turbine Price Drivers

The individual impacts of each of the seven drivers of wind turbine prices examined in Sections 3.1 through 3.7 are summarized in Table 5. To focus attention on the overall trends, the table presents cumulative impacts over the two periods of major turbine price movements in the past decade – i.e., the doubling in turbine prices from 2002-2008, and the subsequent softening in prices through 2010. Figure 13, meanwhile, shows yearly impacts, but of all seven drivers combined rather than individually; this aggregate impact (blue line with circle markers) is plotted against the empirical turbine price curve shown earlier in Figure 2 (red line with star markers).

In aggregate, these seven drivers explain nearly $600/kW of the ~$750/kW increase in average turbine prices observed from 2002-2008, and nearly $90/kW of the ~$195/kW decrease in 2009 and 2010 (Table 5, Figure 13). From 2003 through 2010, the bottom-up analysis of these seven drivers explains between 68% and 89% (depending on the year) of the cumulative empirical price movements (Figure 13).25 Though by no means perfect, this track record improves upon several earlier efforts (Carbon Trust 2008, Greenacre et al. 2010) that have typically failed to quantify more than 60% of observed turbine price increases.

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Nonetheless, Figure 13 shows a growing gap between modeled and empirical turbine prices starting in 2005 and increasing through 2007, at which point the gap remains more or less constant until eventually narrowing in 2010. Some portion of this wedge could potentially be explained by our omission of what were likely increasing labor costs and profitability among component suppliers beginning around 2005 (discussed in Section 3.3). The magnitude of the gap – maxing out at $140/kW – is not out of line with changes in turbine manufacturer labor costs and profitability, which rose by a combined $150/kW through 2008 (see Table 5). Alternatively, or in addition, some portion of the gap could reflect higher exchange-rate pass through than the 50% assumed here, or turbine design and engineering improvements beyond the scaling effects that were analyzed earlier. Finally, some of the discrepancy may simply be due to methodological issues, such as the necessary reliance on Vestas data in this analysis, when the market was supplied by multiple turbine vendors over this period, and especially by GE Wind.

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Ignoring these other potential influences not captured here, Table 5 shows that the four partially endogenous drivers (+$376/kW) still account for more of the turbine price increase from 2002-08 than do the three partially exogenous drivers (+$219/kW). This result suggests that, even absent the considerable exogenous shocks, endogenous price drivers still would have confounded the traditional, simple application of learning curve theory by pushing turbine prices higher even as global wind power installations doubled twice over this period. It is, however, important to note that roughly half of the endogenous impact (+$184/kW) is attributable to turbine up-scaling, for which there is a direct payback in terms of a lower cost of electricity. In other words, from an LCOE perspective, the considerable capital cost impact from turbine up-scaling is not troubling, suggesting that learning effects for wind power should, arguably, be measured through LCOE rather than the more traditionally used turbine prices (or installed project costs).

Finally, it is clear from this analysis that there is no single, dominant factor that drove turbine prices higher from 2002-08, or that has yielded lower prices since that time. Turbine up-scaling is, by a significant margin, the largest single driver, although as noted above, the estimated price impact associated with up-scaling can be seen as a reasonable expense given the performance improvements garnered by larger turbines. Currency movements are also found to have played a sizable – though somewhat uncertain – role, as did changes in labor costs and material prices. Changes in manufacturer profit margins, warranty provisions, and energy prices are found to play a less significant, but non-negligible role.

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Looking Ahead

The analysis described in Section 3, with results summarized and discussed in Section 4, extends through 2010. At the time of writing, however, 2011 is already more than half passed, begging the question of how turbine prices have moved so far in 2011 and how the drivers highlighted in this paper have been impacting those prices. Vestas (2011d) reports that Vestas’ average (nominal) price on new orders worldwide during the first half of 2011 was 967 EUR/kW, down 2.5% from 992 EUR/kW in 2010. Bloomberg NEF (2011c) suggests a steeper decline, as the price of turbines within its sample that were contracted in the first half of 2011 for delivery (worldwide) in the coming twelve months fell to 940 EUR/kW, a 7% decline from 2010 levels of around 1,000 EUR/kW. Bloomberg NEF (2011c) goes on to note that the U.S. is a lower cost market than most of Europe, with average pricing pegged at $1,100/kW (i.e., consistent with the 2011 turbine price range presented earlier in Figure 2). Declining labor costs and warranty provisions appear to be enabling some portion of the decline (Vestas 2011d), while ongoing compression of profit margins among turbine manufacturers and component suppliers may be another contributor (Bloomberg NEF 2011c, Hauser 2011, Weiss and Schneeweiss 2011). And, with a presumed continued up-scaling in turbine size in 2011 compared to 2010, the reported price declines have occurred in concert with larger turbines with presumably enhanced performance.

At the same time, however, all three exogenous drivers examined in this report – changes in raw materials prices, energy prices, and foreign exchange rates – were pressuring turbine prices higher as of mid-year (Figures 9, 10, and 12 – which show real changes in materials prices, energy prices, and foreign exchange rates, respectively – all extend through June 2011). In addition, as of mid-September 2011, some market participants were noting that turbine prices in the U.S. had begun to move somewhat higher as a result of the rush to have wind turbines under contract before the end of the year in order to qualify for the Section 1603 Treasury cash grant.26 Thus, it is not yet clear whether the turbine price reductions seen during the first half of 2011 in comparison to 2010 will persist through the second half of the year, or how the full-year 2011 numbers will compare to 2010.

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The apparent late-year rush induced by the pending expiration of the Section 1603 grant program does, however, highlight another turbine price driver – though of an indirect nature – not yet explicitly discussed in this report: policy risk. The short-term, start-and-stop policy support for wind power that has existed in the U.S. since the production tax credit (“PTC”) first expired in mid-1999 created inefficiencies, artificial demand shocks, and sub-optimal investment through at least 2005 (Wiser et al. 2007). In part as a result, in 2005 when the PTC was for the first time extended for two years in advance of expiration, the resulting surge in demand led to major supply bottlenecks, higher labor costs, and rising profit margins – all of which (together with the other endogenous and exogenous cost pressures examined in this report) pushed turbine prices higher. By the time the industry eventually caught up to demand through increased investments in manufacturing and supply chain infrastructure, the global financial crisis of 2008/2009 had wrought significant demand destruction, leaving newly built manufacturing plants operating well below capacity in some cases (Bloomberg NEF 2011c). The Section 1603 program, with its offering of a 30% cash grant in lieu of the PTC, helped to restore demand to a degree, but is now – along with the PTC –also nearing expiration, with no clear guidance as to what, if anything, might replace it.

Having ramped up manufacturing capacity in local markets in order to meet demand while also minimizing transport costs and mitigating the risk of adverse exchange rate movements, the industry is now more robust than it was in 2005, which should enable it to focus more on driving the cost of wind energy lower, regardless of the policy environment. Even still, whether the cost of wind energy continues down the long-term downward-sloping cost curve from which it departed in 2002 – but with which it has recently re-engaged – may ultimately depend on what types of policy support are put in place post-2012. Long-term, stable policy support – even if it includes a scheduled ramp down over time to progressively wean the industry off of public support – should enable the industry to capitalize on the investments that it has already made while also planning for the future. A continuation of short-term, stop-and-go policy support, on the other hand, may lead to further rounds of artificial and inefficient demand shocks, with consequent impacts on wind turbine and wind energy pricing.